1. Field of the Invention
The present invention generally relates to semiconductor manufacturing processes, and more particularly for improving the accuracy of critical dimension measurement processes.
2. Description of the Related Art
The general environment of semiconductor manufacturing related to lithography and etching has two fundamental components: process control and product compliance. More precisely, in the past, product compliance testing could be accomplished by making a critical dimension (CD) measurement after the lithographic patterning process. When the product failed to comply compared with defined standards, the CD measurement deviation from the target value could be used to change the stepper dose in the rework step. This use of the CD measurement to feed back information to the lithographic process is referred to as process control. To use CD measurements to control the lithography dose requires calibrations. That is, it is necessary to quantitatively determine the correlation between the dosage and the CD.
Currently, patterning processes are much more demanding than previous processes. In order to handle a smaller process tolerance, there are several issues which need to be addressed. The lithography process now has two significant variables that need control: dosage and focus. Furthermore, the use of the resist pattern on the semiconductor substrate wafer in subsequent processing steps (etching, doping, plating, etc . . . ) requires even more structural information, such as the measurement of the resist structure at its bottom, the sidewall angle, and the height of the resist, etc . . .
New demands for compliance monitoring and lithography process control are producing a stream of new innovations. These fall into roughly three categories: better structural measurements for compliance monitoring, better stepper focus and dose monitoring for process control, and techniques that attempt to perform both roles.
Previous critical dimension measurements have been corrupted by structural features, which are not relevant to the critical dimension, but rather are highly sensitive to the stepper focus, like the sidewall angle. Therefore, there is a need for a new method to obtain more accurate critical dimension information by combining the CD measurements with focus monitor information to essentially remove the bias of the undesirable structural components.
As illustrated in FIG. 1, using a simulation code, the waveform peaks for edges with various structural elements is calculated. The ideal edge has a sharp top corner, vertical sidewall, and no bottom foot. The cases considered here include a sidewall deviating from vertical by 0.5 degrees, a rounded top corner with a radius of 50 nm, and a small square foot of 5 nm. Some of these changes affect the location of the critical edge, and some do not. However, all affect the waveform.
As mentioned, the control of semiconductor manufacturing processes like lithography and etching requires the ability to measure critical dimensions of features accurately and precisely. The trend to smaller geometries and more complex designs is challenging the capabilities of CD metrology instruments. On the one hand, the best critical dimension scanning electron microscopes (CDSEM) can show an acceptable single tool precision for many manufacturing levels. However, these same tools are failing to accurately measure the changes due to process drifts. This can result in both false positive (passing bad products) and false negative (failing good products) calls, which have serious financial impacts.
An example illustrating this problem is associated with FIG. 2. This figure shows CDSEM measurements of a post develop isolated raised structure (line) across a focus-and-exposure matrix (FEM) for two leading CDSEMs versus measurements from a respected reference measurement system (RMS). This is a particularly important geometry and material because it is similar to a key semiconductor processing step that determines the speed with which transistors can switch (microprocessor gate level). Tighter and more accurate control at this step of manufacturing can produce computer chips that are extremely fast and profitable. In this case, the RMS is a 2D scanning atomic force microscope (AFM). The AFM measurements identify the critical edge point as the bottom of the structure.
Ideally the data should lie along a straight line with unity slope and zero offset. The scatter of the data around the best fit line is an important measurement quality captured by a quality metric called nonlinearity. Quantitatively, this is proportional to the variance of the scatter. The nonlinearity is normalized such that if all of the variance is due to the random measurement variance measured by reproducibility, then the nonlinearity equals unity. In this case, both CDSEMs have nonlinearities greater than 100. Both are disturbingly large numbers. The single tool precisions for these two CDSEMs are 1.5 nm and 1.8 nm, respectively. Those skilled in the art recognize that, currently, the necessary precision needed for isolated line control is 1.8 nm. The two instruments appear to satisfy this precision requirement but they fail to accurately track the critical dimension changes on the FEM.
In the particular example of the resist isolated line, the problem is associated with severe resist loss during the printing process. This can have profound changes to the line shape. FIG. 3 shows AFM linescans for one of the features on this FEM wafer. The AFM linescans show edge roughness, and even undercut.
These AFM line scans illustrate some of the problems confounding the conventional CDSEM measurement. Typically, the CDSEM directs the electron beam vertically downward on the feature. The beam enters only a small distance into the sample producing secondary electrons, some of these escape the sample and are detected. The basic CDSEM data, called a waveform or linescan, is this detected secondary electron signal versus a primary electron scan position. It is important to note that portions of the feature geometry that are shielded from the electron beam will not be detected. This is illustrated FIG. 11 where cross sections of possible geometries are illustrated. The vertical arrows represent the electron beam hitting the exposed surfaces as it is scanned over the sample. Ideally, the sidewalls should be vertical with sharp corners at the top and bottom. Sometimes the printed feature has undercut sidewalls where the SEM waveform is nearly the same as in the case of ideal sidewalls. Conventional methodologies would falsely report the basewidth measurement of case C the same as case B. The AFM linescans are trusted measurements of the actual feature cross section. The actual AFM linescans of FIG. 3 show an undercut feature with further additional features that can also confuse conventional algorithms.
FIG. 4 shows the variation in feature height and sidewall angle across the FEM, inherent in the prior art approaches. The horizontal axis provides for the stepper focus setting. As shown on the graph, across this FEM, the feature height changes by a factor of three, from approximately 450 nm to 150 nm. There is a significant sidewall angle variation as well, measuring from approximately 0° to almost 14°.
The multiple simultaneous feature changes as a function of stepper focus pose significant challenges for the CD algorithm. Normally, it is the basewidth of the feature that should be reported. These additional changes in the feature geometry should be ignored in the measurement process (data gathering and analysis).
The fundamental assumption underlying the CD measurement is that the location of the true edge of a structure on the semiconductor wafer is associated with some feature or property of the data acquired by the CD measuring instrument. In the case of the CDSEM, the data is usually a one-dimensional waveform or linescan resulting from detecting the secondary electrons generated by sweeping a primary electron beam across the structure. Monte Carlo simulations have indicated that the edge base of the structure (usually identified as the critical edge location) is associated with the outside maximum or minimum slope of the waveform as illustrated in FIG. 5, which was generated using the following parameters: electron beam conditions: 500 eV landing energy, and 10 nm spot size.
This figure shows the results from a prior art Monte Carlo simulation program, Monsel-II. The SEM detected signal is approximately proportional to the secondary electron yield. The SEM operates by scanning the structure from above. The trapezoidal example structure is shown in dotted lines from a side view. Notably, when the primary beam is traversing the left or right edge of the structure there is an increase in the secondary electron yield. With this simplified model of the printed pattern, the derivative of the secondary electron yield shows peaks closely associated with the base of the trapezoidal structure. A common prior art method reports the distance between these outer extreme slope locations as the critical dimension of the structure.
Currently, more and less sophisticated methods are available on most CDSEMs. Generally, they focus on analyzing the signal peak for each edge in some manner. For example, a threshold method could allow the user to define a percentage intensity along the outside portion of the peak to declare as the structure critical edge location. The extreme slope locations may correspond to approximately a 50% threshold. In order to overcome signal-to-noise issues, some methods fit portions of the peak to a functional form and then use a user-selectable threshold to determine the critical edge point.
Another reason to use a greater portion of the peak waveform data and to increase the flexibility for choosing the critical edge point location is to be able to handle the real manufacturing problems of resist footing, corner rounding, edge roughness, and more. These more flexibly defined methods are empirical in nature and a calibration exercise of some sort is necessary to set the parameters. Despite these complications, the operating paradigm is that somewhere in the waveform there is a feature associated with the true critical edge location. This could be called the true-critical-edge paradigm.
Together with the true-critical-edge paradigm there is another common assumption used in selecting the operating conditions and method parameters. This assumption is that the measurement system (beam conditions and method settings) should be optimized to give the smallest single tool precision and smallest measurement offset to a respected reference measurement system. This second assumption has been recently challenged as not emphasizing accuracy enough. In any case, the goal of the prior art is to optimize the measurement system in some manner where the method available has been developed under the assumption of the true-critical-edge paradigm.
Optimizing for minimum precision and even the smallest offset actually are deviations from the true-critical-edge paradigm. This is because selecting method parameters to improve the precision may shift the point in the waveform to associate with the edge away from the true edge to a location with better signal stability. Furthermore, to shift the point in the waveform in order to minimize the offset ignores a very significant offset introduced by the physics of the SEM operation. Nevertheless, the practitioners of this methodology generally ignore the corruption of the true-critical-edge paradigm when performing these compromises as these are generally thought of as minor adjustments. That is, the thinking is that the selected point in the waveform chosen should still be a satisfactory mimic of the true edge location.
Other prior art approaches, such as the Davidson approach (J. S. Villarrubia, A. E. Vladar, J. R. Lowney, M. T. Postek, Jr., “Edge determination for polysilicon lines on gate oxide”, Proceedings of SPIE 2001, Volume 4344, Paper 4344-21), to be published, have made a notable exception to this paradigm. By using a unique SEM simulation code, a library of waveforms can be generated corresponding to sample geometries and materials as well as SEM operating conditions. These can then be searched at the time of measurement to identify the best match. The system then reports the critical dimension associated with this best match. Unfortunately, this approach may prove to be too background intensive and has not yet demonstrated an adequate ability to deal with sample charging.